Parallel Magnetic Resonance Imaging (MRI) works by acquiring a reduced amount of k-space data with an array of receiver coils. Increasing the number of receiver coils increases signal-to-noise ratios therefore improving reconstructed image quality. However, processing of data from a large set of independent receiver coils leads to increased memory and computational load in reconstruction. These increases can be especially problematic for 3D acquisitions and in iterative reconstructions. Coil compression techniques are effective in mitigating this problem by compressing redundant data from many physical coils into fewer virtual coils.
Conventional coil compression techniques take the k-space dataset (with dimensions of 2D, 2D+Time, 3D or 3D+Time) and compress the number of coils globally. Initially, k-space data is processed into a two-dimensional matrix where one dimension is all the data from one coil and the other dimension is all coils. Then, a singular variable decomposition (SVD) is used to reduce the dimension along the coil dimension (the second dimension), thus reducing the number of total coils. This results in a new k-space dataset, with a smaller number of virtual coils, which is the starting point for the reconstruction.
Recently, coil compression techniques have been introduced to exploit the spatially varying coil sensitivities in non-subsampled dimensions for better compression and computation reduction. Although these techniques offer promising results, they are designed for Cartesian acquisition schemes. Thus, they are not readily extended to non-Cartesian acquisition schemes employed in many parallel imaging applications. In addition, the existing methods only consider spatial variation along the readout dimension in MR acquisition, and ignore the possibility to explore spatial variation along the other spatial dimensions. Accordingly, it is desired to create a spatially varying coil compression technique that can be applied in any clinical scenario.